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What Motives Users to Participate in Danmu on Live Streaming Platforms? The Impact of Technical Environment and Effectance

1 Introduction Live streaming has become a widespread phenomenon across the world. The streamer can broadcast a variety of content on live streaming platforms ranging from gaming to dancing and singing without any geographic barriers. Viewers can directly chat with the streamer or other viewers by sending real-time comments on the live streaming, aka Danmu. This potential of real-time interaction and influencing the live stream has led to widespread use of live streaming services. Data from a study conducted by Interactive Advertising Bureau (IAB) show that

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Filtering and Classifying Relevant Short Text with a Few Seed Words

, cancer, disease, healthy, nutrition, diet, hospital, physical, therapy, diagnosis, treatment, care, heart, fitness, medicine, food, symptoms, hepatitis, clinical 21.5 6.4 Politics Society political, democracy, party, government, republic, military, democratic, politics, united, president, parliamentary, freedom, national, congress, social, communist, revolution, parties, civil, presidential 18.7 4.2 Sports sports, football, games, game, tennis, match, league, espn, tickets, team, players, hockey, sport, olympic, basketball, golf, cup, volleyball

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Business Values/Implications of AI and Machine Learning

, people have been talking about AI since the 1950s. Let us consider Alan Turing, who is considered the father of computing, and John McCarthy at MIT and Marvin Minsky, who developed complicated thinking machines. When the three of them got together and started talking about AI, Alan Turing proposed what was called the “imitation game;” there was also a movie about this. The imitation game essentially entailed a human dealing with an agent that he/she could not see. The human talked to the agent and then had to decide whether it was a human or not. If a computer could

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Big Data in Health Care: Applications and Challenges

threats of gaming the system from attempting to increase the risk score of patients through subjective variables. Kennedy et al. (2013) conducted a retrospective cohort study. In this paper, they identified all Veterans Health Administration (VHA) patients without recent cerebral and cardiovascular (CCV) events treated at twelve facilities from 2003 to 2007 and predicted risk using the Framingham risk score (FRS), logistic regression, generalized additive modeling, and gradient tree boosting. Oztekin et al. (2009) used three different variable selection

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